OpenRaiser/Intern-Atlas huggingface dataset:
Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?
Venue: arXiv cs.CV 2017
Year: 2017

We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data. Experimental results on three challenging real-world material databases show that the best performing pre-trained convolutional neural network (CNN) architectures can achieve up to 91.03% mean average precision when classifying materials in cross-dataset scenarios. We demonstrate that synthesized data achieve an improvement on mean average precision when used as training data and in conjunction with pre-trained CNN architectures, which spans from ~ 5% to ~ 19% across three widely used material databases of real-world images.
In-Depth Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations
Venue: arXiv cs.DC 2017
Year: 2017

In situ lossy compression allowing user-controlled data loss can significantly reduce the I/O burden. For large-scale N-body simulations where only one snapshot can be compressed at a time, the lossy compression ratio is very limited because of the fairly low spatial coherence of the particle data. In this work, we assess the state-of-the-art single-snapshot lossy compression techniques of two common N-body simulation models: cosmology and molecular dynamics. We design a series of novel optimization techniques based on the two representative real-world N-body simulation codes. For molecular dynamics simulation, we propose three compression modes (i.e., best speed, best tradeoff, best compression mode) that can refine the tradeoff between the compression rate (a.k.a., speed/throughput) and ratio. For cosmology simulation, we identify that our improved SZ is the best lossy compressor with respect to both compression ratio and rate. Its compression ratio is higher than the second-best compressor by 11% with comparable compression rate. Experiments with up to 1024 cores on the Blues supercomputer at Argonne show that our proposed lossy compression method can reduce I/O time by 80% compared with writing data directly to a parallel file system and outperforms the second-best solution by 60%. Moreover, our proposed lossy compression methods have the best rate-distortion with reasonable compression errors on the tested N-body simulation data compared with state-of-the-art compressors.
Group Connectivity: $\mathbb Z_4$ v. $\mathbb Z_2^2$
Venue: arXiv cs.DM 2017
Year: 2017

We answer a question on group connectivity suggested by Jaeger et al. [Group connectivity of graphs -- A nonhomogeneous analogue of nowhere-zero flow properties, JCTB 1992]: we find that $\mathbb Z_2^2$-connectivity does not imply $\mathbb Z_4$-connectivity, neither vice versa. We use a computer to find the graphs certifying this and to verify their properties using non-trivial enumerative algorithm. While the graphs are small (the largest has 15 vertices and 21 edges), a computer-free approach remains elusive.
Towards ECDSA key derivation from deep embeddings for novel Blockchain applications
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we propose a straightforward method to derive Elliptic Curve Digital Signature Algorithm (ECDSA) key pairs from embeddings created using Deep Learning and Metric Learning approaches. We also show that these keys allows the derivation of cryptocurrencies (such as Bitcoin) addresses that can be used to transfer and receive funds, allowing novel Blockchain-based applications that can be used to transfer funds or data directly to domains such as image, text, sound or any other domain where Deep Learning can extract high-quality embeddings; providing thus a novel integration between the properties of the Blockchain-based technologies such as trust minimization and decentralization together with the high-quality learned representations from Deep Learning techniques.
3D Randomized Connection Network with Graph-based Label Inference
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are employed as network units to capture the long-term and short-term 3D properties respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on two publicly available databases and results demonstrate that the proposed method can obtain competitive performances as compared with other state-of-the-art methods.
Analytic Model for Network Resource Management between ISPs and Users
Venue: arXiv cs.NI 2017
Year: 2017

Fixed Communication Provider (FCP) is a consortium of Internet Service Providers (ISPs) which users can switch easily and freely between their ISPs. In order to increase the QoS of the ISPs, we propose a two class service model as the following. ISPs divide their available bandwidth into two parts to provide their end users with optimal services. One dedicated to primary users, and the other for secondary users. Primary users are those who pay more and thus, expect dedicated bandwidth that is always available. Secondary services are provided by ISPs for the other users who cannot afford the dedicated bandwidth. In this study, by defining the utility functions for both user types, we aim at dividing the ISP bandwidth between these two services such that the utility function of the users is maximized. Since the primary users do not always use the maximum bandwidth, an algorithm is proposed for ISPs to estimate the primary users required bandwidth in each time segment based on the previous segments. Based on this estimate, the expected bandwidth is dedicated to the primary users, and the remaining bandwidth is devoted to the secondary users to improve the Quality of Service (QoS). On the other hand, an ISP is penalized if it fails to provide the primary users with the required bandwidth mentioned in their contract. Therefore, there exists a trade-off between the conservative estimation of the primary users rate, QoS of the secondary users and the achieved utility of the ISPs.
Assessing Agile Transformation Success Factors
Venue: arXiv cs.SE 2017
Year: 2017

Research on success factors involved in the agile transformation process is not conclusive and there is still need for guidelines to help in the transformation process considering the organizational context (culture, values, needs, reality and goals). The usage of success factors as a tool to help agile adoption raises the following research question: What are the success factors for an organization and their teams in preparation for the agile transformation process? This research presents an assessment of the organizational environment including the company's goals and the perception of the team members to provide awareness of how the organization should prepare for the next steps in the agile transformation. The findings show that a company based in Chicago, USA, succeeded implementing customer involvement and self-organized teams but faces challenges with measurement models and training. The main contribution of the research is understand which success factors exist in their environment and how they can be used during agile adoption.
Building machines that adapt and compute like brains
Venue: arXiv cs.AI 2017
Year: 2017

Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains. A new cognitive computational neuroscience should build cognitive-level and neural- level models, understand their relationships, and test both types of models with both brain and behavioral data.
Strongly Secure and Efficient Data Shuffle On Hardware Enclaves
Venue: arXiv cs.CR 2017
Year: 2017

Mitigating memory-access attacks on the Intel SGX architecture is an important and open research problem. A natural notion of the mitigation is cache-miss obliviousness which requires the cache-misses emitted during an enclave execution are oblivious to sensitive data. This work realizes the cache-miss obliviousness for the computation of data shuffling. The proposed approach is to software-engineer the oblivious algorithm of Melbourne shuffle on the Intel SGX/TSX architecture, where the Transaction Synchronization eXtension (TSX) is (ab)used to detect the occurrence of cache misses. In the system building, we propose software techniques to prefetch memory data prior to the TSX transaction to defend the physical bus-tapping attacks. Our evaluation based on real implementation shows that our system achieves superior performance and lower transaction abort rate than the related work in the existing literature.
Evaluation of trackers for Pan-Tilt-Zoom Scenarios
Venue: arXiv cs.CV 2017
Year: 2017

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. Compared to tracking with a still camera, the images captured with a PTZ camera are highly dynamic in nature because the camera can perform large motion resulting in quickly changing capture conditions. Furthermore, tracking with a PTZ camera involves camera control to position the camera on the target. For successful tracking and camera control, the tracker must be fast enough, or has to be able to predict accurately the next position of the target. Therefore, standard benchmarks do not allow to assess properly the quality of a tracker for the PTZ scenario. In this work, we use a virtual PTZ framework to evaluate different tracking algorithms and compare their performances. We also extend the framework to add target position prediction for the next frame, accounting for camera motion and processing delays. By doing this, we can assess if predicting can make long-term tracking more robust as it may help slower algorithms for keeping the target in the field of view of the camera. Results confirm that both speed and robustness are required for tracking under the PTZ scenario.
Hand Gesture Recognition with Leap Motion
Venue: arXiv cs.CV 2017
Year: 2017

The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multi-class SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.
Fast Reading Comprehension with ConvNets
Venue: arXiv cs.CL 2017
Year: 2017

State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelization within an instances and often becomes the bottleneck for deploying such models to latency critical scenarios. This is particularly problematic for longer texts. Here we present a convolutional architecture as an alternative to these recurrent architectures. Using simple dilated convolutional units in place of recurrent ones, we achieve results comparable to the state of the art on two question answering tasks, while at the same time achieving up to two orders of magnitude speedups for question answering.
Dynamic Algorithms for Graph Coloring
Venue: arXiv cs.DS 2017
Year: 2017

We design fast dynamic algorithms for proper vertex and edge colorings in a graph undergoing edge insertions and deletions. In the static setting, there are simple linear time algorithms for $(Δ+1)$- vertex coloring and $(2Δ-1)$-edge coloring in a graph with maximum degree $Δ$. It is natural to ask if we can efficiently maintain such colorings in the dynamic setting as well. We get the following three results. (1) We present a randomized algorithm which maintains a $(Δ+1)$-vertex coloring with $O(\log Δ)$ expected amortized update time. (2) We present a deterministic algorithm which maintains a $(1+o(1))Δ$-vertex coloring with $O(\text{poly} \log Δ)$ amortized update time. (3) We present a simple, deterministic algorithm which maintains a $(2Δ-1)$-edge coloring with $O(\log Δ)$ worst-case update time. This improves the recent $O(Δ)$-edge coloring algorithm with $\tilde{O}(\sqrtΔ)$ worst-case update time by Barenboim and Maimon.
Visual Concepts and Compositional Voting
Venue: arXiv cs.CV 2017
Year: 2017

It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large.
Sparsification of the Alignment Path Search Space in Dynamic Time Warping
Venue: arXiv cs.LG 2017
Year: 2017

Temporal data are naturally everywhere, especially in the digital era that sees the advent of big data and internet of things. One major challenge that arises during temporal data analysis and mining is the comparison of time series or sequences, which requires to determine a proper distance or (dis)similarity measure. In this context, the Dynamic Time Warping (DTW) has enjoyed success in many domains, due to its 'temporal elasticity', a property particularly useful when matching temporal data. Unfortunately this dissimilarity measure suffers from a quadratic computational cost, which prohibits its use for large scale applications. This work addresses the sparsification of the alignment path search space for DTW-like measures, essentially to lower their computational cost without loosing on the quality of the measure. As a result of our sparsification approach, two new (dis)similarity measures, namely SP-DTW (Sparsified-Paths search space DTW) and its kernelization SP-K rdtw (Sparsified-Paths search space K rdtw kernel) are proposed for time series comparison. A wide range of public datasets is used to evaluate the efficiency (estimated in term of speed-up ratio and classification accuracy) of the proposed (dis)similarity measures on the 1-Nearest Neighbor (1-NN) and the Support Vector Machine (SVM) classification algorithms. Our experiment shows that our proposed measures provide a significant speed-up without loosing on accuracy. Furthermore, at the cost of a slight reduction of the speedup they significantly outperform on the accuracy criteria the old but well known Sakoe-Chiba approach that reduces the DTW path search space using a symmetric corridor.
Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation
Venue: arXiv cs.MS 2017
Year: 2017

Massively parallel accelerators such as GPGPUs, manycores and FPGAs represent a powerful and affordable tool for scientists who look to speed up simulations of complex systems. However, porting code to such devices requires a detailed understanding of heterogeneous programming tools and effective strategies for parallelization. In this paper we present a source to source compilation approach with whole-program analysis to automatically transform single-threaded FORTRAN 77 legacy code into OpenCL-accelerated programs with parallelized kernels. The main contributions of our work are: (1) whole-source refactoring to allow any subroutine in the code to be offloaded to an accelerator. (2) Minimization of the data transfer between the host and the accelerator by eliminating redundant transfers. (3) Pragmatic auto-parallelization of the code to be offloaded to the accelerator by identification of parallelizable maps and reductions. We have validated the code transformation performance of the compiler on the NIST FORTRAN 78 test suite and several real-world codes: the Large Eddy Simulator for Urban Flows, a high-resolution turbulent flow model; the shallow water component of the ocean model Gmodel; the Linear Baroclinic Model, an atmospheric climate model and Flexpart-WRF, a particle dispersion simulator. The automatic parallelization component has been tested on as 2-D Shallow Water model (2DSW) and on the Large Eddy Simulator for Urban Flows (UFLES) and produces a complete OpenCL-enabled code base. The fully OpenCL-accelerated versions of the 2DSW and the UFLES are resp. 9x and 20x faster on GPU than the original code on CPU, in both cases this is the same performance as manually ported code.
Stable project allocation under distributional constraints
Venue: arXiv cs.GT 2017
Year: 2017

In a two-sided matching market when agents on both sides have preferences the stability of the solution is typically the most important requirement. However, we may also face some distributional constraints with regard to the minimum number of assignees or the distribution of the assignees according to their types. These two requirements can be challenging to reconcile in practice. In this paper we describe two real applications, a project allocation problem and a workshop assignment problem, both involving some distributional constraints. We used integer programming techniques to find reasonably good solutions with regard to the stability and the distributional constraints. Our approach can be useful in a variety of different applications, such as resident allocation with lower quotas, controlled school choice or college admissions with affirmative action.
A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we propose a convergent parallel best-response algorithm with the exact line search for the nondifferentiable nonconvex sparsity-regularized rank minimization problem. On the one hand, it exhibits a faster convergence than subgradient algorithms and block coordinate descent algorithms. On the other hand, its convergence to a stationary point is guaranteed, while ADMM algorithms only converge for convex problems. Furthermore, the exact line search procedure in the proposed algorithm is performed efficiently in closed-form to avoid the meticulous choice of stepsizes, which is however a common bottleneck in subgradient algorithms and successive convex approximation algorithms. Finally, the proposed algorithm is numerically tested.
United Nations Digital Blue Helmets as a Starting Point for Cyber Peacekeeping
Venue: arXiv cs.CY 2017
Year: 2017

Prior works, such as the Tallinn manual on the international law applicable to cyber warfare, focus on the circumstances of cyber warfare. Many organizations are considering how to conduct cyber warfare, but few have discussed methods to reduce, or even prevent, cyber conflict. A recent series of publications started developing the framework of Cyber Peacekeeping (CPK) and its legal requirements. These works assessed the current state of organizations such as ITU IMPACT, NATO CCDCOE and Shanghai Cooperation Organization, and found that they did not satisfy requirements to effectively host CPK activities. An assessment of organizations currently working in the areas related to CPK found that the United Nations (UN) has mandates and organizational structures that appear to somewhat overlap the needs of CPK. However, the UN's current approach to Peacekeeping cannot be directly mapped to cyberspace. In this research we analyze the development of traditional Peacekeeping in the United Nations, and current initiatives in cyberspace. Specifically, we will compare the proposed CPK framework with the recent initiative of the United Nations named the 'Digital Blue Helmets' as well as with other projects in the UN which helps to predict and mitigate conflicts. Our goal is to find practical recommendations for the implementation of the CPK framework in the United Nations, and to examine how responsibilities defined in the CPK framework overlap with those of the 'Digital Blue Helmets' and the Global Pulse program.
COMBINE: a novel drug discovery platform designed to capture insight and experience of users
Venue: arXiv cs.HC 2017
Year: 2017

The insight and experience gained by a researcher are often lost because the current productive and analytics software are inherently data-centric, disconnected, and scattered. The connected nature of insight and experience can be captured if the applications themselves are connected. How connected applications concept is implemented in COnstruct cheMical and BIological NEtwork (COMBINE), a novel user-centric drug discovery platform, is described. Using publicly available data, how COMBINE users capture insight and experience is explained, and how COMBINE users perform data organization, data sharing, data analysis, and data visualization is illustrated.
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017. We propose an ensemble of several models, which capture spatial and audio features from videos. Spatial features are captured by convolutional neural networks, pretrained on large face recognition datasets. We show that usage of strong industry-level face recognition networks increases the accuracy of emotion recognition. Using our ensemble we improve on the previous best result on the test set by about 1 %, achieving a 60.03 % classification accuracy without any use of visual temporal information.